1. Technical Field
One or more embodiments of the present invention relate to image attribute discrimination processing of discriminating an attribute of digital-format image data such as a still image and a moving image, particularly to an image attribute discrimination apparatus, an attribute discrimination support apparatus, an image attribute discrimination method, an attribute discrimination support apparatus controlling method, and a control program for improving accuracy of the image attribute discrimination processing.
2. Related Art
Recently, there is conducted research and development of a technique of analyzing a feature of image data to automatically discriminate an attribute of the image data. Specifically, a feature quantity is extracted from a pixel value possessed by any piece of image data such as the still image or moving image which is imaged with a digital camera, a digital video camera, or a camera-equipped mobile phone, the still image captured by a scanner, and the moving image or capture image which is recorded by a DVD recorder, and a scene (attribute) expressed by the image data is discriminated. For example, what kind of scene (such as person, landscape, night view, sunset, firework, room interior, snow, beach, flower, cooking, and business card and document) taken by the real-time image data processed by the digital camera is discriminated, which allows a photograph to be taken while a photographing mode of the digital camera is set to an optimum state according to the scene.
For example, Japanese Unexamined Patent Publication Nos. 11-298736 (published on Oct. 29, 1999), 2002-218480 (published on Aug. 2, 2002), 2005-310123 (published on Nov. 4, 2005), and 2005-122720 (published on May 12, 2005) disclose known image attribute discrimination processing techniques. In the techniques disclosed in Japanese Unexamined Patent Publication Nos. 11-298736, 2002-218480, 2005-310123, and 2005-122720, the feature quantity is extracted from target digital image data to perform processing of checking the feature quantity against a previously-prepared model feature quantity with respect to a specific scene, and a scene is discriminated based on a degree of coincidence with the feature quantity of the specific scene.
More specifically, in an image processing apparatus disclosed in Japanese Unexamined Patent Publication No. 11-298736, a determination whether the image data is the sunset scene is made using a histogram of hue data, and a determination whether the image data needs to be corrected is made based on the determination whether the image data is the sunset scene. The image processing apparatus makes the histograms of a value a product of the hue and chroma and a value of a product of the hue and lightness with respect to the pixels belonging to a range of red to yellow in the pixels constituting the target image data, and the image processing apparatus determines that a variance of the histogram that is larger than a specific reference is the image of the scene “sunset”.
Japanese Unexamined Patent Publication No. 2002-218480 discloses an image photographing apparatus that discriminates a plurality of scenes such as “portrait”, “sunset”, and “night view” with respect to the target image data with information on the presence or absence of a person and information on a color histogram as a common feature index.
Japanese Unexamined Patent Publication No. 2005-310123 discloses an apparatus that accurately selects various images of specific scenes with respect to a feature portion corresponding to the specific scene in consideration of tendency of disposition in the image and in consideration of a position of photographing frame and a variation of area ratio by a photographing frame taking difference.
In an apparatus disclosed in Japanese Unexamined Patent Publication No. 2005-122720, reference data in which a kind of the feature quantity and an identifying condition are defined is prepared in each of the plurality of scenes designated as the specific scene in order to identify the scene, and scene discrimination is accurately performed by referring to the identifying condition.
However, in the conventional configurations, unfortunately the attribute cannot correctly be discriminated when a substance, a shadow, and a shape which are different from those of the original attribute of the image data (hereinafter referred to as a heterogeneous matter) are included in the image data that becomes the attribute discrimination target. That is, the feature obtained from a pixel group (hereinafter referred to as a heterogeneous region) taking the heterogeneous matter is different from the feature of the original attribute. Therefore, when the feature quantity of the whole image data is extracted while the feature quantity of the heterogeneous region is mixed in the feature quantity of the image data, the checking against the model feature quantity is not successfully performed, which results in false scene discrimination is performed to the image data or scene discrimination is performed with low likelihood.
For example, the generation of the heterogeneous region in the image data is attributed to objects, (also includes telop in the case of the moving image) such as a character, an illustration, a graphic, a stamp, and a graffiti, which are added to the image data that becomes the attribute discrimination target using an image edit tool in an image edit process. Additionally, sometimes an unintended phenomenon (such as a white spot phenomenon such as smear) emerges in the image data photographing process depending on a photographing environment or a subject state or an intended body such as a finger shadow is taken in the photograph. Additionally, sometimes an original plate or a backside color of an original is taken in a lack portion of the original in the process of scanning the origin such as the photograph (due to the broken original or folded original). The heterogeneous region is not limited to the above-described examples. The above problems are commonly generated irrespective of the condition, environment, and situation relating to the image data, when the attribute discrimination is performed to any piece of image data including the heterogeneous matter whose attribute is different from that of the original scene.